DNase-seq (DNase I hypersensitive sites sequencing) is a method in molecular biology used to identify the location of regulatory regions, based on the genome-wide sequencing of regions sensitive to cleavage by DNase I.[1][2][3] FAIRE-Seq is a successor of DNase-seq for the genome-wide identification of accessible DNA regions in the genome. Both the protocols for identifying open chromatin regions have biases depending on underlying nucleosome structure. For example, FAIRE-seq provides higher tag counts at non-promoter regions.[4] On the other hand, DNase-seq signal is higher at promoter regions, and DNase-seq has been shown to have better sensitivity than FAIRE-seq even at non-promoter regions.[4]

DNase-seq Footprinting

DNase-seq requires some downstream bioinformatics analyses in order to provide genome-wide DNA footprints. The computational tools proposed can be categorized in two classes: segmentation-based and site-centric approaches. Segmentation-based methods are based on the application of Hidden Markov models or sliding window methods to segment the genome into open/closed chromatin region. Examples of such methods are: HINT,[5] Boyle method[6] and Neph method.[7] Site-centric methods, on the other hand, find footprints given the open chromatin profile around motif-predicted binding sites, i.e., regulatory regions predicted using DNA-protein sequence information (encoded in structures such as Position weight matrix). Examples of these methods are CENTIPEDE[8] and Cuellar-Partida method.[9]

References

  1. ↑ Boyle, AP; Davis S; Shulha HP; Meltzer P; Margulies EH; Weng Z; Furey TS; Crawford GE (2008). "High-resolution mapping and characterization of open chromatin across the genome". Cell. 132 (2): 311–22. doi:10.1016/j.cell.2007.12.014. PMC 2669738. PMID 18243105.
  2. ↑ Crawford, GE; Holt, IE; Whittle, J; Webb, BD; Tai, D; Davis, S; Margulies, EH; Chen, Y; Bernat, JA; Ginsburg, D; Zhou, D; Luo, S; Vasicek, TJ; Daly, MJ; Wolfsberg, TG; Collins, FS (January 2006). "Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS)". Genome Research. 16 (1): 123–131. doi:10.1101/gr.4074106. PMC 1356136. PMID 16344561.
  3. ↑ Madrigal, P; Krajewski, P (October 2012). "Current bioinformatic approaches to identify DNase I hypersensitive sites and genomic footprints from DNase-seq data". Front Genet. 3: 230. doi:10.3389/fgene.2012.00230. PMC 3484326. PMID 23118738.
  4. 1 2 Prabhakar S., Vibhor Kumar; Rayan NA; Kraus P; Lufkin T; Ng HH (July 2013). "Uniform, optimal signal processing of mapped deep-sequencing data". Nature Biotechnology. 31 (7): 615–22. doi:10.1038/nbt.2596. PMID 23770639.
  5. ↑ Gusmao, EG; Dieterich, C; Zenke, M; Costa, IG (Aug 2014). "Detection of Active Transcription Factor Binding Sites with the Combination of DNase Hypersensitivity and Histone Modifications". Bioinformatics. 30 (22): 3143–51. doi:10.1093/bioinformatics/btu519. PMID 25086003.
  6. ↑ Boyle, AP; et al. (Mar 2011). "High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells". Genome Research. 21 (3): 456–464. doi:10.1101/gr.112656.110. PMC 3044859. PMID 21106903.
  7. ↑ Neph, S; et al. (Sep 2012). "An expansive human regulatory lexicon encoded in transcription factor footprints". Nature. 489 (7414): 83–90. Bibcode:2012Natur.489...83N. doi:10.1038/nature11212. PMC 3736582. PMID 22955618.
  8. ↑ Pique-Regi, R; et al. (Mar 2011). "Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data". Genome Research. 21 (3): 447–455. doi:10.1101/gr.112623.110. PMC 3044858. PMID 21106904.
  9. ↑ Cuellar-Partida, G; et al. (Jan 2012). "Epigenetic priors for identifying active transcription factor binding sites". Bioinformatics. 28 (1): 56–62. doi:10.1093/bioinformatics/btr614. PMC 3244768. PMID 22072382.
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